Service providers (e.g., wireless, cellular, etc.) and device manufacturers are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. A popular application is delivering services to a user of a wireless device based on the device's location, and many mobile devices include Global Positioning System (GPS) receivers that provide geolocation of the device to about 30 meter accuracy. While suitable for many purposes, there are problems delivering services based on GPS data. One problem is that the power consumption in continuously monitoring on-device GPS is high. It is still not possible for most of the mobile phones to continuously trace a user for more than a relatively short period (e.g., 3 hours) without charging. A second problem is that the storage cost for storing GPS traces is also high. A third problem is that it is still not efficient and effective to derive a context for a user, e.g., at or near a usual spot (such as café, gym, work or home) based on determining similarity of different GPS traces. It is desirable to derive context, such as “work” or “lunch” or “recreation,” to be used to tailor network services. However, current techniques for deriving location context from geolocation data are computationally intensive and do not scale well to thousands of users of the service.